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R

by Yashaswini D. Mittal

1 What is R

R is open domain statistical software based on the language S. Open domain language S was originally written at Bell-Labs. S-Plus is a commercial statistical software also based on S. BOTH S-Plus and R are also programming languages and hence allow one to do innovative things with the data.
R is open domain and downloadable from the CRAN site at (size about 24 MB for Windows version)

The current version is 2.0.1. Make sure to install the reference manual. The site can be used to download many other packages that address particular statistical methodologies. However, the packages included in the basic download are extensive enough for all but the most specialized user. Even though it was written by statisticians for use in statistics, it can be used for many other functions as well. It has superior graphical facility.

S-Plus is more user-friendly, more buggy, more illogical and more expensive than R. The two are similar but not the same. In R, many more things need to be done with commands rather than with a drop down menu as in S-Plus. Both programs are unix based at heart though versions of both are available for Windows, Unix, and the Mac.

2 Getting Started

Double click on the R icon to load the program and use

to quit. Make sure to choose the option ”No” when it asks whether to save workspace image or not. If any newly created objects are saved once, they would need to be deleted manually later. Command objects() will show all objects in the working directory and rm(x,y) will remove objects x,y from the working directory.

R does not automatically load all available commands. Various commands are grouped into packages according to particular themes. Most common use will need nothing more than what gets loaded automatically. Other packages that are in the background, can be loaded with

will load the package MASS which gets loaded in the background. Each time the R session is closed, the added packages disappear from the working directory. A loaded package can be detached without closing the session with

Other packages are available from the site according to Other R Projects. For example, the Geostatistics subpart of the R spatial projects have additional 7 packages listed that can be downloaded first and then loaded in the R Console or Working Directory.

3 Help

R gives a sub-menu that contains FAQ on R, FAQ on R for Windows as well as Introduction to R and the Manuals.

will load the Acrobat Reader. Explanation of the command may appear somewhat cryptic initially, but it gives examples and hence is quite useful. One can also use e.g. help.search("time series") to get information on the topic of time series. It will list number of commands used in time series analysis along with the name of the package in which the commands exist. (It is not an exhaustive list.) Command ?cor can be used to produce information on how to compute the correlation coefficientt because cor(,) is a valid S command. Command ?regression will give an error message because regression is not a valid S command.

4 Objects and Data Types

The language S uses several forms of data such as vectors, matrices, arrays, dataframes and lists. Each type of data has different properties. S is object oriented and acts on vectors but built-in functions are particular about what type of data can be used as the input. Commands such as

will say T if the object m is a matrix and F if it is not. The command data.class(m) will give result "matrix" if m is a matrix. Data can be forced into other forms than its own under certain circumstances. A particular command may not work because the data is in a wrong format.

5 Creating Data

Easiest type of data to create is a vector. For example,

Command

will prompt for input.

Command

will make a 4x4 matrix.

6 Spread Sheet in Data Window

In R, an empty spread sheet named test can be obtained with commands

After entering the data, the updated fille test gets automatically saved by simply closing it. The default column names Var1,Var2,... can be changed by clicking on the names. Column names can also be added to an existing fille test with the command

Once an object named test exists,

will bring it up and one can edit the cells. However, if the edited version needs to be saved, reassign it using

and then close the edited spread sheet. It is possible to invoke various other editors for the spread sheet. Find help with ?edit.
The data set can be exported with the command

7 Importing Data

One needs to use different protocols for importing different types of files. Easiest to import is a .txt filele that follows the R conventions. Ironically, R is NOT case sensitive in the command read.table().
The option header=T MUST be used if and only if the columns have names. Otherwise the data imports incorrectly.

8 Resident Data Sets

Several data sets automatically get loaded at the time of loading the package. These serve as examples in various Help items. These are the only data sets that can be accessed without having to import them first in the working directory. A list of the resident data sets can be obtained with

The data set USArrests is brought in the working directory with command data(USArrests) and then it can be renamed. By attach(USArrests), all the names of the columns can be recognized by their names.

9 Graphics

Some demos of graphs from the base package can be viewed with command demo(graphics). All graphs need to be drawn with commands and each graphics command has plenty of options to fine tune one’s graph. A graphics screen can be split by par(mfrow=c(2,3)). Successive graphs will be filled by row.

Existing graphs can be augmented with commands such as lines(x,y) or abline(h=c(y1,y2,y3)), etc. Text, symbols and legends can be inserted in an existing graph. Commands locator(), or identify() will make an active cursor in the existing graph and by successively clicking on desired points, we can finnd the co-ordinates of those points.

Commonly used graphs can be drawn with commands plot(x,y), hist(x),boxplot(air), etc.

10 Missing Values and Logical Operations

Missing values need to be handled in computational commands. The option na.rm=T removes missing values for most of the functions such as mean, median, var, etc. For functions such as cor, use option use="complete.obs". Missing values from the entire data set can be removed by na.exclude(air). Many graphics commands use this option as the default.

If x and y be two vectors of same length. Then y[y == x] will give a sub-vector of y such that y[i]= x[i]. y[y!=x[1]] will give all values of y not equal to x[1]. We can extract sub-matrices such as m[m[,2]> 13 & m[,4]< 38,].

11 Standard Distributions

For a list of available probability distributions and their R names, see table on page 37 of the Introduction to R.

will get the list. For each one of these distributions the probability density, distribution function, quantiles, and simulation of the distribution is obtained by adding respectively d, p, q and r behind the R name of the distribution. The table also gives the parameters of these distributions. Any arbitrary discrete distribution can be simulated with sample(x,50,replace=T,prob=p).

12 Programming in R

The greatest payoff of R is that it is also a programming language. Small programs can be written in a Notepad filele and then run in the working directory by copying and pasting the program. For example, the function meanunif simulates nU(0,1) random variables, computes its mean, and repeats the process N times.

meanunif <-function(n,N){
  x<-1:N 
  for (i in 1:N){x[i]<-mean(runif(n))} 
  x 
}